





'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A_		:  Optional[int]		 =						logging.get_logger(__name__)



def        UpperCamelCase__		(						__magic_name__  :   Optional[Any]		,       __magic_name__  :   str=False      )				->			Tuple:
 '''simple docstring'''






 snake_case__     :					int    =  []

 # fmt: off
 # stem:
 rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")      )
 rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")      )

 rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")      )
 rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")      )

 # backbone
 rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")      )
 rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")      )
 rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")      )

 for stage_idx in range(len(config.backbone_config.depths      )      ):
  for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")      )

  rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")      )
  rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")      )
  rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")      )

 # transformer encoder
 for i in range(config.num_hidden_layers      ):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")      )
  rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")      )
  rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")      )
  rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")      )
  rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")      )
  rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")      )
  rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")      )
  rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")      )
  rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")      )
  rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")      )

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ("""norm.weight""", """layernorm.weight"""),
          ("""norm.bias""", """layernorm.bias"""),
          ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
          ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
      ]      )

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  snake_case__     :					List[Any]    =  [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""      ) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("""norm.weight""", """vit.layernorm.weight"""),
          ("""norm.bias""", """vit.layernorm.bias"""),
          ("""head.weight""", """classifier.weight"""),
          ("""head.bias""", """classifier.bias"""),
      ]      )
 # fmt: on

 return rename_keys



def        UpperCamelCase__		(						__magic_name__  :   Tuple		,       __magic_name__  :   int		,       __magic_name__  :   Tuple=False      )				->			str:
 '''simple docstring'''






 for i in range(config.num_hidden_layers      ):
  if base_model:
   snake_case__     :					int    =  """"""
  else:
   snake_case__     :					Dict    =  """vit."""
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  snake_case__     :					int    =  state_dict.pop(f"blocks.{i}.attn.qkv.weight"      )
  snake_case__     :					Union[str, Any]    =  state_dict.pop(f"blocks.{i}.attn.qkv.bias"      )
  # next, add query, keys and values (in that order) to the state dict
  snake_case__     :					Optional[int]    =  in_proj_weight[
      : config.hidden_size, :
  ]
  snake_case__     :					Optional[Any]    =  in_proj_bias[: config.hidden_size]
  snake_case__     :					List[Any]    =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  snake_case__     :					List[Any]    =  in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  snake_case__     :					List[Any]    =  in_proj_weight[
      -config.hidden_size :, :
  ]
  snake_case__     :					Optional[int]    =  in_proj_bias[-config.hidden_size :]



def        UpperCamelCase__		(						__magic_name__  :   Optional[Any]      )				->			List[str]:
 '''simple docstring'''






 snake_case__     :					str    =  ["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(__magic_name__		,       __magic_name__      )



def        UpperCamelCase__		(						__magic_name__  :   List[str]		,       __magic_name__  :   Union[str, Any]		,       __magic_name__  :   str      )				->			Union[str, Any]:
 '''simple docstring'''






 snake_case__     :					List[str]    =  dct.pop(__magic_name__      )
 snake_case__     :					Dict    =  val



def        UpperCamelCase__		(						)				->			str:
 '''simple docstring'''






 snake_case__     :					Optional[int]    =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
 snake_case__     :					Optional[int]    =  Image.open(requests.get(__magic_name__		,       stream=__magic_name__      ).raw      )
 return im



@torch.no_grad()
def        UpperCamelCase__		(						__magic_name__  :   List[Any]		,       __magic_name__  :   Union[str, Any]		,       __magic_name__  :   int=False      )				->			Optional[int]:
 '''simple docstring'''






 snake_case__     :					int    =  BitConfig(
     global_padding="""same"""		,       layer_type="""bottleneck"""		,       depths=(3, 4, 9)		,       out_features=["""stage3"""]		,       embedding_dynamic_padding=__magic_name__		,       )
 snake_case__     :					Optional[int]    =  ViTHybridConfig(backbone_config=__magic_name__		,       image_size=3_84		,       num_labels=10_00      )
 snake_case__     :					Union[str, Any]    =  False

 # load original model from timm
 snake_case__     :					List[Any]    =  timm.create_model(__magic_name__		,       pretrained=__magic_name__      )
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 snake_case__     :					Optional[int]    =  timm_model.state_dict()
 if base_model:
  remove_classification_head_(__magic_name__      )
 snake_case__     :					int    =  create_rename_keys(__magic_name__		,       __magic_name__      )
 for src, dest in rename_keys:
  rename_key(__magic_name__		,       __magic_name__		,       __magic_name__      )
 read_in_q_k_v(__magic_name__		,       __magic_name__		,       __magic_name__      )

 snake_case__     :					str    =  """huggingface/label-files"""
 snake_case__     :					Union[str, Any]    =  """imagenet-1k-id2label.json"""
 snake_case__     :					Dict    =  json.load(open(hf_hub_download(__magic_name__		,       __magic_name__		,       repo_type="""dataset"""      )		,       """r"""      )      )
 snake_case__     :					List[Any]    =  {int(__magic_name__      ): v for k, v in idalabel.items()}
 snake_case__     :					int    =  idalabel
 snake_case__     :					str    =  {v: k for k, v in idalabel.items()}

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  snake_case__     :					str    =  ViTHybridModel(__magic_name__      ).eval()
 else:
  snake_case__     :					Union[str, Any]    =  ViTHybridForImageClassification(__magic_name__      ).eval()
 model.load_state_dict(__magic_name__      )

 # create image processor
 snake_case__     :					Optional[Any]    =  create_transform(**resolve_data_config({}		,       model=__magic_name__      )      )
 snake_case__     :					Union[str, Any]    =  transform.transforms

 snake_case__     :					Tuple    =  {
     """bilinear""": PILImageResampling.BILINEAR,
     """bicubic""": PILImageResampling.BICUBIC,
     """nearest""": PILImageResampling.NEAREST,
 }

 snake_case__     :					Any    =  ViTHybridImageProcessor(
     do_resize=__magic_name__		,       size={"""shortest_edge""": timm_transforms[0].size}		,       resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,       do_center_crop=__magic_name__		,       crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}		,       do_normalize=__magic_name__		,       image_mean=timm_transforms[-1].mean.tolist()		,       image_std=timm_transforms[-1].std.tolist()		,       )

 snake_case__     :					Any    =  prepare_img()
 snake_case__     :					int    =  transform(__magic_name__      ).unsqueeze(0      )
 snake_case__     :					List[str]    =  processor(__magic_name__		,       return_tensors="""pt"""      ).pixel_values

 # verify pixel values
 assert torch.allclose(__magic_name__		,       __magic_name__      )

 # verify logits
 with torch.no_grad():
  snake_case__     :					Optional[Any]    =  model(__magic_name__      )
  snake_case__     :					Union[str, Any]    =  outputs.logits

 print("""Predicted class:"""		,       logits.argmax(-1      ).item()      )
 if base_model:
  snake_case__     :					Dict    =  timm_model.forward_features(__magic_name__      )
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(__magic_name__		,       outputs.pooler_output		,       atol=1E-3      )
 else:
  snake_case__     :					int    =  timm_model(__magic_name__      )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(__magic_name__		,       outputs.logits		,       atol=1E-3      )
 print("""Looks ok!"""      )

 if pytorch_dump_folder_path is not None:
  Path(__magic_name__      ).mkdir(exist_ok=__magic_name__      )
  print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"      )
  model.save_pretrained(__magic_name__      )
  print(f"Saving processor to {pytorch_dump_folder_path}"      )
  processor.save_pretrained(__magic_name__      )

 if push_to_hub:
  print(f"Pushing model and processor to the hub {vit_name}"      )
  model.push_to_hub(f"ybelkada/{vit_name}"      )
  processor.push_to_hub(f"ybelkada/{vit_name}"      )


if __name__ == "__main__":
    A_		:  List[str]		 =						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--vit_name",
        default="vit_base_r50_s16_384",
        type=str,
        help="Name of the hybrid ViT timm model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
    )

    A_		:  Union[str, Any]		 =						parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:

    def count_of_possible_combinations(UpperCamelCase__							) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item							) for item in array							)

    return count_of_possible_combinations(UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase__							,    UpperCamelCase__							) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        __lowerCamelCase            =    sum(
            count_of_possible_combinations_with_dp_array(target - item							,    UpperCamelCase__							)
            for item in array							)
        __lowerCamelCase            =    answer
        return answer

    __lowerCamelCase            =    [-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase__							,    UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    [0] * (target + 1)
    __lowerCamelCase            =    1

    for i in range(1							,    target + 1							):
        for j in range(UpperCamelCase__							):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __UpperCAmelCase					=3
  __UpperCAmelCase					=5
  __UpperCAmelCase					=[1, 2, 5]
  print(combination_sum_iv(n, array, target))
